Flowminder & IDB release their guide for developing high-resolution socioeconomic maps

Ending poverty in all its forms everywhere by 2030 and ensuring that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracy are some of the key goals and targets set in the 2030 Agenda for Sustainable Development

As the progress toward the realisation of the SDGs may differ greatly between areas of a given country, public policies interventions aimed at realising the SDGs everywhere need to be targeted and calibrated to local conditions and monitored accordingly. Mapping SDG indicators at high spatial resolution could hence play a central tool for meeting the Sustainable Development Goals (SDGs).

The Inter-American Development Bank (IDB) and Flowminder partnered to produce a web book that provides a tutorial for the production of socioeconomic maps, using the R programming language and geolocated information from satellite images or other sources, using data from El Salvador as the use case. The tool guides from the pre-processing of the data to the final calculation of the map.

The collaboration was set up in 2019 and funded by the IDB to produce high-resolution poverty, income, and literacy maps for El Salvador. Thanks to IDB collaboration with the  Salvadoran National Statistics Office, Dygestic, existing household survey data were used and combined by Flowminder with free and open source remote sensing and geographic information (GIS) system data (such as on rainfall, temperature, and vegetation) to better understand agricultural productivity, or light at night and distance to roads and cities. These data were combined with household surveys into a statistical model to produce maps at high spatial resolution of poverty, income and literacy.

As a result of this collaboration, IDB released a step-by-step tutorial on how to produce comprehensive high-resolution poverty maps from raw data. The emphasis of the project is on the use of the R programming language for Bayesian geostatistical models, the work being fully reproducible, free and open source. 

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